Environmental inFluences on the Adoption of Open Innovation: Analysis of Structural, Institutional and Cultural Impacts

The concept of open innovation has recently gained widespread attention, and is particularly relevant now as many firms endeavouring to implement open innovation, face different sets of challenges associated with managing it. Prior research on open innovation has focused on the internal processes dealing with open innovation implementation and the organizational changes, already taking place or yet required in companies order to succeed in the global open innovation market. Despite the intensive research on open innovation, the question of what influences its adoption by companies in different contexts has not received much attention in studies. To fill this gap, this thesis contribute to the discussion on open innovation influencing factors by bringing in the perspective of environmental impacts, i.e. gathering data on possible sources of external influences, classifying them and testing their systemic impact through conceptual system dynamics simulation model. The insights from data collection and conceptualization in modelling are used to answer the question of how the external environment affects the adoption of open innovation. The thesis research is presented through five research papers reflecting the method triangulation based study (conducted at initial stage as case study, later as quantitative analysis and finally as system dynamics simulation). This multitude of methods was used to collect the possible external influence factors and to assess their impact (on positive/negative scale rather than numerical). The results obtained throughout the thesis research bring valuable insights into understanding of open innovation influencing factors inside a firm’s operating environment, point out the balance required in the system for successful open innovation performance and discover the existence of tipping point of open innovation success when driven by market dynamics and structures. The practical implications on how firms and policy-makers can leverage environment for their potential benefits are offered in the conclusions.

Methods for construction and analysis of computational models in systems biology : applications to the modelling of the heat shock response and the self-assembly of intermediate filaments

Systems biology is a new, emerging and rapidly developing, multidisciplinary research field that aims to study biochemical and biological systems from a holistic perspective, with the goal of providing a comprehensive, system- level understanding of cellular behaviour. In this way, it addresses one of the greatest challenges faced by contemporary biology, which is to compre- hend the function of complex biological systems. Systems biology combines various methods that originate from scientific disciplines such as molecu- lar biology, chemistry, engineering sciences, mathematics, computer science and systems theory. Systems biology, unlike “traditional” biology, focuses on high-level concepts such as: network, component, robustness, efficiency, control, regulation, hierarchical design, synchronization, concurrency, and many others. The very terminology of systems biology is “foreign” to “tra- ditional” biology, marks its drastic shift in the research paradigm and it indicates close linkage of systems biology to computer science. One of the basic tools utilized in systems biology is the mathematical modelling of life processes tightly linked to experimental practice. The stud- ies contained in this thesis revolve around a number of challenges commonly encountered in the computational modelling in systems biology. The re- search comprises of the development and application of a broad range of methods originating in the fields of computer science and mathematics for construction and analysis of computational models in systems biology. In particular, the performed research is setup in the context of two biolog- ical phenomena chosen as modelling case studies: 1) the eukaryotic heat shock response and 2) the in vitro self-assembly of intermediate filaments, one of the main constituents of the cytoskeleton. The range of presented approaches spans from heuristic, through numerical and statistical to ana- lytical methods applied in the effort to formally describe and analyse the two biological processes. We notice however, that although applied to cer- tain case studies, the presented methods are not limited to them and can be utilized in the analysis of other biological mechanisms as well as com- plex systems in general. The full range of developed and applied modelling techniques as well as model analysis methodologies constitutes a rich mod- elling framework. Moreover, the presentation of the developed methods, their application to the two case studies and the discussions concerning their potentials and limitations point to the difficulties and challenges one encounters in computational modelling of biological systems. The problems of model identifiability, model comparison, model refinement, model inte- gration and extension, choice of the proper modelling framework and level of abstraction, or the choice of the proper scope of the model run through this thesis.